ArticleJournal of nephrology2025
Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies.
Article in Journal of nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Time-Zero Kidney Biopsy and Deceased-Donor Transplant Outcomes: A Systematic Review and Meta-Analysis of Prognostic Value, Prediction, and Clinical Utility.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
- Application and exploration of digital pathology technology in standardized residency training.BMC medical education · 2026Article
- Mobile device smartphones for intraoperative diagnosis at the University Hospital Trust of Modena/UNIMORE: from validation process to costs analysis.Diagnostic pathology · 2026Article
- Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies.Scientific reports · 2025Article
- "Pathologists in Venice - Real World Cases for an Immersive Training Experience": Education, Gaming, and Show.Journal of pathology informatics · 2025Article
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14 authors.
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Abstract
backgroundPre-transplant procurement biopsy interpretation is challenging, also because of the low number of renal pathology experts. Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsy assessment. Herein we present the "Galileo" AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies.
methodsA multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm.
resultsGalileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2 min overall in the validation phase (vs 25, 22 and 31 min by 3 separate human readers, p < 0.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report.
conclusionsThe Galileo AI-assisted tool shows promise in speeding up pre-implantation kidney biopsy interpretation, as well as in reducing inter-observer variability. This tool may represent a starting point for further improvements based on hard endpoints such as graft survival.
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